Overview

Dataset statistics

Number of variables16
Number of observations1165
Missing cells8199
Missing cells (%)44.0%
Duplicate rows37
Duplicate rows (%)3.2%
Total size in memory145.8 KiB
Average record size in memory128.1 B

Variable types

Categorical4
Numeric12

Alerts

Dataset has 37 (3.2%) duplicate rowsDuplicates
Excipients_1 has a high cardinality: 93 distinct valuesHigh cardinality
Application Rate (%) is highly overall correlated with Excipients_1High correlation
initial TN(%) is highly overall correlated with initial CN(%)High correlation
initial TC(%) is highly overall correlated with CO2-C loss (%)High correlation
initial CN(%) is highly overall correlated with initial TN(%)High correlation
TN loss (%) is highly overall correlated with NH3-N loss (%)High correlation
NH3-N loss (%) is highly overall correlated with TN loss (%)High correlation
N2O-N loss (%) is highly overall correlated with TC loss (%)High correlation
TC loss (%) is highly overall correlated with N2O-N loss (%) and 1 other fieldsHigh correlation
CO2-C loss (%) is highly overall correlated with initial TC(%) and 1 other fieldsHigh correlation
material_0 is highly overall correlated with material_1 and 1 other fieldsHigh correlation
material_1 is highly overall correlated with material_0 and 1 other fieldsHigh correlation
Excipients_1 is highly overall correlated with Application Rate (%) and 2 other fieldsHigh correlation
material_0 has 87 (7.5%) missing valuesMissing
material_1 has 409 (35.1%) missing valuesMissing
Excipients_1 has 498 (42.7%) missing valuesMissing
Additive Species has 810 (69.5%) missing valuesMissing
Application Rate (%) has 948 (81.4%) missing valuesMissing
initial moisture content(%) has 256 (22.0%) missing valuesMissing
initial pH has 306 (26.3%) missing valuesMissing
initial TN(%) has 214 (18.4%) missing valuesMissing
initial TC(%) has 222 (19.1%) missing valuesMissing
initial CN(%) has 262 (22.5%) missing valuesMissing
TN loss (%) has 558 (47.9%) missing valuesMissing
NH3-N loss (%) has 460 (39.5%) missing valuesMissing
N2O-N loss (%) has 631 (54.2%) missing valuesMissing
TC loss (%) has 795 (68.2%) missing valuesMissing
CH4-C loss (%) has 769 (66.0%) missing valuesMissing
CO2-C loss (%) has 974 (83.6%) missing valuesMissing

Reproduction

Analysis started2024-04-23 06:03:41.111250
Analysis finished2024-04-23 06:03:50.287217
Duration9.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

material_0
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.6%
Missing87
Missing (%)7.5%
Memory size9.2 KiB
Manure
673 
Sewage sludge
185 
Lignin
87 
Food waste
86 
Digestate
 
43

Length

Max length13
Median length6
Mean length7.6363636
Min length5

Characters and Unicode

Total characters8232
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManure
2nd rowManure
3rd rowManure
4th rowManure
5th rowManure

Common Values

ValueCountFrequency (%)
Manure 673
57.8%
Sewage sludge 185
 
15.9%
Lignin 87
 
7.5%
Food waste 86
 
7.4%
Digestate 43
 
3.7%
Other 4
 
0.3%
(Missing) 87
 
7.5%

Length

2024-04-23T14:03:50.334585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T14:03:50.405492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manure 673
49.9%
sewage 185
 
13.7%
sludge 185
 
13.7%
lignin 87
 
6.4%
food 86
 
6.4%
waste 86
 
6.4%
digestate 43
 
3.2%
other 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1404
17.1%
a 987
12.0%
u 858
10.4%
n 847
10.3%
r 677
8.2%
M 673
8.2%
g 500
 
6.1%
s 314
 
3.8%
w 271
 
3.3%
271
 
3.3%
Other values (11) 1430
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6883
83.6%
Uppercase Letter 1078
 
13.1%
Space Separator 271
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1404
20.4%
a 987
14.3%
u 858
12.5%
n 847
12.3%
r 677
9.8%
g 500
 
7.3%
s 314
 
4.6%
w 271
 
3.9%
d 271
 
3.9%
i 217
 
3.2%
Other values (4) 537
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
M 673
62.4%
S 185
 
17.2%
L 87
 
8.1%
F 86
 
8.0%
D 43
 
4.0%
O 4
 
0.4%
Space Separator
ValueCountFrequency (%)
271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7961
96.7%
Common 271
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1404
17.6%
a 987
12.4%
u 858
10.8%
n 847
10.6%
r 677
8.5%
M 673
8.5%
g 500
 
6.3%
s 314
 
3.9%
w 271
 
3.4%
d 271
 
3.4%
Other values (10) 1159
14.6%
Common
ValueCountFrequency (%)
271
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1404
17.1%
a 987
12.0%
u 858
10.4%
n 847
10.3%
r 677
8.2%
M 673
8.2%
g 500
 
6.1%
s 314
 
3.8%
w 271
 
3.3%
271
 
3.3%
Other values (11) 1430
17.4%

material_1
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)1.7%
Missing409
Missing (%)35.1%
Memory size9.2 KiB
Swine manure
319 
Cow manure
172 
Poultry manure
131 
Manure
43 
Digestate
 
28
Other values (8)
63 

Length

Max length14
Median length13
Mean length11.058201
Min length5

Characters and Unicode

Total characters8360
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSwine manure
2nd rowSwine manure
3rd rowSwine manure
4th rowSwine manure
5th rowCow manure

Common Values

ValueCountFrequency (%)
Swine manure 319
27.4%
Cow manure 172
14.8%
Poultry manure 131
 
11.2%
Manure 43
 
3.7%
Digestate 28
 
2.4%
OFMSW 25
 
2.1%
Yard Waste 13
 
1.1%
Sludge 10
 
0.9%
Horse manure 5
 
0.4%
Food Waste 5
 
0.4%
Other values (3) 5
 
0.4%
(Missing) 409
35.1%

Length

2024-04-23T14:03:50.462369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manure 673
48.0%
swine 319
22.7%
cow 172
 
12.3%
poultry 131
 
9.3%
digestate 28
 
2.0%
ofmsw 25
 
1.8%
waste 18
 
1.3%
yard 13
 
0.9%
sludge 10
 
0.7%
horse 5
 
0.4%
Other values (3) 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 1083
13.0%
n 994
11.9%
r 824
9.9%
u 816
9.8%
a 734
8.8%
650
7.8%
m 631
7.5%
w 491
 
5.9%
S 354
 
4.2%
i 347
 
4.2%
Other values (17) 1436
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6836
81.8%
Uppercase Letter 874
 
10.5%
Space Separator 650
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1083
15.8%
n 994
14.5%
r 824
12.1%
u 816
11.9%
a 734
10.7%
m 631
9.2%
w 491
7.2%
i 347
 
5.1%
o 318
 
4.7%
t 207
 
3.0%
Other values (6) 391
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
S 354
40.5%
C 172
19.7%
P 131
 
15.0%
M 69
 
7.9%
W 43
 
4.9%
F 30
 
3.4%
D 28
 
3.2%
O 27
 
3.1%
Y 13
 
1.5%
H 7
 
0.8%
Space Separator
ValueCountFrequency (%)
650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7710
92.2%
Common 650
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1083
14.0%
n 994
12.9%
r 824
10.7%
u 816
10.6%
a 734
9.5%
m 631
8.2%
w 491
6.4%
S 354
 
4.6%
i 347
 
4.5%
o 318
 
4.1%
Other values (16) 1118
14.5%
Common
ValueCountFrequency (%)
650
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1083
13.0%
n 994
11.9%
r 824
9.9%
u 816
9.8%
a 734
8.8%
650
7.8%
m 631
7.5%
w 491
 
5.9%
S 354
 
4.2%
i 347
 
4.2%
Other values (17) 1436
17.2%

Excipients_1
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct93
Distinct (%)13.9%
Missing498
Missing (%)42.7%
Memory size9.2 KiB
Corn stalk
133 
Sawdust
91 
Wheat straw
76 
Rice straw
40 
Plant straw
 
24
Other values (88)
303 

Length

Max length55
Median length37
Mean length12.242879
Min length4

Characters and Unicode

Total characters8166
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)5.7%

Sample

1st rowCorn stalk
2nd rowCorn stalk
3rd rowCorn stalk
4th rowCorn stalk
5th rowSawdust

Common Values

ValueCountFrequency (%)
Corn stalk 133
 
11.4%
Sawdust 91
 
7.8%
Wheat straw 76
 
6.5%
Rice straw 40
 
3.4%
Plant straw 24
 
2.1%
Cornstalk 23
 
2.0%
Mushroom residue 17
 
1.5%
Rice husk 13
 
1.1%
Sawdust 12
 
1.0%
Dariy manure + Tomato stalks 11
 
0.9%
Other values (83) 227
19.5%
(Missing) 498
42.7%

Length

2024-04-23T14:03:50.525321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
straw 173
 
13.0%
corn 138
 
10.4%
stalk 136
 
10.2%
sawdust 117
 
8.8%
wheat 84
 
6.3%
rice 66
 
5.0%
manure 39
 
2.9%
37
 
2.8%
waste 34
 
2.6%
residue 33
 
2.5%
Other values (105) 472
35.5%

Most occurring characters

ValueCountFrequency (%)
a 891
 
10.9%
t 768
 
9.4%
s 764
 
9.4%
684
 
8.4%
r 621
 
7.6%
e 564
 
6.9%
w 390
 
4.8%
u 385
 
4.7%
o 374
 
4.6%
n 289
 
3.5%
Other values (34) 2436
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6642
81.3%
Uppercase Letter 727
 
8.9%
Space Separator 684
 
8.4%
Math Symbol 101
 
1.2%
Other Punctuation 8
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 891
13.4%
t 768
11.6%
s 764
11.5%
r 621
9.3%
e 564
8.5%
w 390
 
5.9%
u 385
 
5.8%
o 374
 
5.6%
n 289
 
4.4%
l 285
 
4.3%
Other values (14) 1311
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 204
28.1%
C 171
23.5%
W 85
11.7%
R 70
 
9.6%
M 58
 
8.0%
P 49
 
6.7%
T 18
 
2.5%
G 16
 
2.2%
B 15
 
2.1%
F 14
 
1.9%
Other values (5) 27
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
. 4
50.0%
Space Separator
ValueCountFrequency (%)
684
100.0%
Math Symbol
ValueCountFrequency (%)
+ 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7369
90.2%
Common 797
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 891
12.1%
t 768
 
10.4%
s 764
 
10.4%
r 621
 
8.4%
e 564
 
7.7%
w 390
 
5.3%
u 385
 
5.2%
o 374
 
5.1%
n 289
 
3.9%
l 285
 
3.9%
Other values (29) 2038
27.7%
Common
ValueCountFrequency (%)
684
85.8%
+ 101
 
12.7%
- 4
 
0.5%
, 4
 
0.5%
. 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 891
 
10.9%
t 768
 
9.4%
s 764
 
9.4%
684
 
8.4%
r 621
 
7.6%
e 564
 
6.9%
w 390
 
4.8%
u 385
 
4.7%
o 374
 
4.6%
n 289
 
3.5%
Other values (34) 2436
29.8%

Additive Species
Categorical

Distinct4
Distinct (%)1.1%
Missing810
Missing (%)69.5%
Memory size9.2 KiB
Physical
227 
Chemical
85 
Biological
28 
Mixture
 
15

Length

Max length10
Median length8
Mean length8.115493
Min length7

Characters and Unicode

Total characters2881
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChemical
2nd rowChemical
3rd rowChemical
4th rowPhysical
5th rowPhysical

Common Values

ValueCountFrequency (%)
Physical 227
 
19.5%
Chemical 85
 
7.3%
Biological 28
 
2.4%
Mixture 15
 
1.3%
(Missing) 810
69.5%

Length

2024-04-23T14:03:50.572156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T14:03:50.772187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
physical 227
63.9%
chemical 85
 
23.9%
biological 28
 
7.9%
mixture 15
 
4.2%

Most occurring characters

ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2526
87.7%
Uppercase Letter 355
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 383
15.2%
l 368
14.6%
c 340
13.5%
a 340
13.5%
h 312
12.4%
y 227
9.0%
s 227
9.0%
e 100
 
4.0%
m 85
 
3.4%
o 56
 
2.2%
Other values (5) 88
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
P 227
63.9%
C 85
 
23.9%
B 28
 
7.9%
M 15
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2881
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Application Rate (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)37.8%
Missing948
Missing (%)81.4%
Infinite0
Infinite (%)0.0%
Mean12.439352
Minimum0.002
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:50.834785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.976
Q15
median10
Q318
95-th percentile32.04
Maximum51
Range50.998
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.9579927
Coefficient of variation (CV)0.80052341
Kurtosis1.5824732
Mean12.439352
Median Absolute Deviation (MAD)5.2
Skewness1.2022293
Sum2699.3394
Variance99.161618
MonotonicityNot monotonic
2024-04-23T14:03:50.912107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 33
 
2.8%
5 19
 
1.6%
23 13
 
1.1%
1 9
 
0.8%
25 6
 
0.5%
33 6
 
0.5%
14 6
 
0.5%
15 6
 
0.5%
20 5
 
0.4%
6 5
 
0.4%
Other values (72) 109
 
9.4%
(Missing) 948
81.4%
ValueCountFrequency (%)
0.002 1
 
0.1%
0.25 3
 
0.3%
0.4 3
 
0.3%
0.409 1
 
0.1%
0.454 1
 
0.1%
0.497 1
 
0.1%
0.88 1
 
0.1%
1 9
0.8%
1.5 1
 
0.1%
1.76 1
 
0.1%
ValueCountFrequency (%)
51 1
 
0.1%
50 1
 
0.1%
47 1
 
0.1%
42 1
 
0.1%
35 1
 
0.1%
33 6
0.5%
31.8 1
 
0.1%
31.3 1
 
0.1%
30 3
0.3%
27 2
 
0.2%
Distinct370
Distinct (%)40.7%
Missing256
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean65.270385
Minimum40
Maximum89.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:50.970540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52.88
Q160
median65
Q370
95-th percentile81.08
Maximum89.8
Range49.8
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1510012
Coefficient of variation (CV)0.12488054
Kurtosis0.5325882
Mean65.270385
Median Absolute Deviation (MAD)5
Skewness0.3839093
Sum59330.78
Variance66.43882
MonotonicityNot monotonic
2024-04-23T14:03:51.033419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 115
 
9.9%
65 93
 
8.0%
55 21
 
1.8%
70 18
 
1.5%
63 17
 
1.5%
61 10
 
0.9%
64 9
 
0.8%
65.4 9
 
0.8%
76.28 9
 
0.8%
58 9
 
0.8%
Other values (360) 599
51.4%
(Missing) 256
22.0%
ValueCountFrequency (%)
40 2
0.2%
43.61 1
0.1%
44 1
0.1%
45 1
0.1%
45 2
0.2%
45.32 1
0.1%
46.3 1
0.1%
46.7 1
0.1%
46.9 2
0.2%
47.5 2
0.2%
ValueCountFrequency (%)
89.8 1
 
0.1%
88.5 7
0.6%
86 1
 
0.1%
85.33 2
 
0.2%
85 3
0.3%
84.89 1
 
0.1%
84.66 2
 
0.2%
84.3 4
0.3%
84.1 1
 
0.1%
84 1
 
0.1%

initial pH
Real number (ℝ)

Distinct376
Distinct (%)43.8%
Missing306
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean7.5032901
Minimum3.51
Maximum10.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:51.111915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.51
5-th percentile5.9
Q17.003182
median7.6
Q38.1
95-th percentile8.8
Maximum10.7
Range7.19
Interquartile range (IQR)1.096818

Descriptive statistics

Standard deviation0.90939034
Coefficient of variation (CV)0.12119888
Kurtosis0.99300558
Mean7.5032901
Median Absolute Deviation (MAD)0.52
Skewness-0.50184861
Sum6445.3262
Variance0.82699078
MonotonicityNot monotonic
2024-04-23T14:03:51.188659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 25
 
2.1%
7.8 23
 
2.0%
7.6 22
 
1.9%
7.1 18
 
1.5%
8 17
 
1.5%
6.5 17
 
1.5%
8.2 16
 
1.4%
7.4 16
 
1.4%
7.845613506 15
 
1.3%
6.9 14
 
1.2%
Other values (366) 676
58.0%
(Missing) 306
26.3%
ValueCountFrequency (%)
3.51 1
0.1%
4.28 1
0.1%
4.42231 1
0.1%
4.43 1
0.1%
4.45 1
0.1%
4.8 1
0.1%
4.81275 1
0.1%
4.92 1
0.1%
5 1
0.1%
5.01 1
0.1%
ValueCountFrequency (%)
10.7 1
0.1%
10.32 2
0.2%
10 1
0.1%
9.8 2
0.2%
9.6 1
0.1%
9.4 1
0.1%
9.35 1
0.1%
9.33 1
0.1%
9.2 1
0.1%
9.19 1
0.1%

initial TN(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct640
Distinct (%)67.3%
Missing214
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean2.34614
Minimum0.37
Maximum11.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:51.243155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.04
Q11.6163244
median1.9902175
Q32.6
95-th percentile4.865
Maximum11.58
Range11.21
Interquartile range (IQR)0.98367557

Descriptive statistics

Standard deviation1.461799
Coefficient of variation (CV)0.62306556
Kurtosis11.537899
Mean2.34614
Median Absolute Deviation (MAD)0.47021745
Skewness3.0411443
Sum2231.1791
Variance2.1368563
MonotonicityNot monotonic
2024-04-23T14:03:51.324741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.845139719 28
 
2.4%
1.9 19
 
1.6%
2.1 13
 
1.1%
2.4 13
 
1.1%
1.4 12
 
1.0%
1.79 8
 
0.7%
1.5 8
 
0.7%
1.7 7
 
0.6%
2.04 7
 
0.6%
2.6 6
 
0.5%
Other values (630) 830
71.2%
(Missing) 214
 
18.4%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.68 2
0.2%
0.69 3
0.3%
0.7 2
0.2%
0.723 1
 
0.1%
0.77 1
 
0.1%
0.782 1
 
0.1%
0.794 1
 
0.1%
0.8 2
0.2%
0.807 1
 
0.1%
ValueCountFrequency (%)
11.58 1
0.1%
11.12 1
0.1%
10.55498413 1
0.1%
10.4 1
0.1%
10.27 1
0.1%
10 1
0.1%
9.79 1
0.1%
9.72 1
0.1%
9.1 1
0.1%
8.8 1
0.1%

initial TC(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct703
Distinct (%)74.5%
Missing222
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean48.693987
Minimum1.45
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:51.387233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile28.010389
Q136.724593
median41.06
Q348.312671
95-th percentile109.54927
Maximum197
Range195.55
Interquartile range (IQR)11.588078

Descriptive statistics

Standard deviation27.229587
Coefficient of variation (CV)0.55919814
Kurtosis9.5182113
Mean48.693987
Median Absolute Deviation (MAD)5.54
Skewness2.9294619
Sum45918.43
Variance741.4504
MonotonicityNot monotonic
2024-04-23T14:03:51.446855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.40765076 22
 
1.9%
38 9
 
0.8%
72.33 8
 
0.7%
46.6 7
 
0.6%
48.99 7
 
0.6%
40 7
 
0.6%
78.27 7
 
0.6%
37.3 7
 
0.6%
36.9 7
 
0.6%
44.89 6
 
0.5%
Other values (693) 856
73.5%
(Missing) 222
 
19.1%
ValueCountFrequency (%)
1.45 3
0.3%
5.8 1
 
0.1%
5.85 1
 
0.1%
6 1
 
0.1%
6.35 1
 
0.1%
19.14 1
 
0.1%
19.152 1
 
0.1%
19.3 1
 
0.1%
20.2 1
 
0.1%
21.8 1
 
0.1%
ValueCountFrequency (%)
197 1
0.1%
196.75 2
0.2%
185 1
0.1%
180.67 1
0.1%
178.23 1
0.1%
177.9 1
0.1%
174.91 1
0.1%
172.4 1
0.1%
168 1
0.1%
163 1
0.1%

initial CN(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct468
Distinct (%)51.8%
Missing262
Missing (%)22.5%
Infinite0
Infinite (%)0.0%
Mean21.873341
Minimum1.1
Maximum55.983471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:51.524809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile9.5104021
Q116
median20.1
Q325.812899
95-th percentile39.709994
Maximum55.983471
Range54.883471
Interquartile range (IQR)9.8128995

Descriptive statistics

Standard deviation9.0815696
Coefficient of variation (CV)0.41518894
Kurtosis1.3589865
Mean21.873341
Median Absolute Deviation (MAD)4.9
Skewness0.95734524
Sum19751.627
Variance82.474906
MonotonicityNot monotonic
2024-04-23T14:03:51.587265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 51
 
4.4%
30 43
 
3.7%
20 29
 
2.5%
18 20
 
1.7%
15 20
 
1.7%
17.8 11
 
0.9%
21 11
 
0.9%
32 10
 
0.9%
19 9
 
0.8%
18.4 8
 
0.7%
Other values (458) 691
59.3%
(Missing) 262
 
22.5%
ValueCountFrequency (%)
1.1 1
 
0.1%
2.536745928 1
 
0.1%
2.901592789 1
 
0.1%
4.43 3
0.3%
4.89 1
 
0.1%
4.916910084 1
 
0.1%
5.22 1
 
0.1%
5.4 1
 
0.1%
6.1 2
0.2%
6.15 1
 
0.1%
ValueCountFrequency (%)
55.98347107 1
0.1%
55 1
0.1%
53.73493976 1
0.1%
53.69565217 1
0.1%
53.37662338 1
0.1%
52.88659794 1
0.1%
52.0212766 1
0.1%
51.70992366 1
0.1%
51.5 1
0.1%
51.20879121 1
0.1%

TN loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct494
Distinct (%)81.4%
Missing558
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean22.970922
Minimum-1
Maximum85.54023
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size9.2 KiB
2024-04-23T14:03:51.660161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2.7425561
Q111.004942
median20.85
Q332.140076
95-th percentile49.597
Maximum85.54023
Range86.54023
Interquartile range (IQR)21.135134

Descriptive statistics

Standard deviation15.101342
Coefficient of variation (CV)0.65741121
Kurtosis0.23315924
Mean22.970922
Median Absolute Deviation (MAD)10.63
Skewness0.68239566
Sum13943.35
Variance228.05052
MonotonicityNot monotonic
2024-04-23T14:03:51.718767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 6
 
0.5%
15.5 5
 
0.4%
35 5
 
0.4%
16.99 4
 
0.3%
27 4
 
0.3%
23 4
 
0.3%
0.5 4
 
0.3%
63 4
 
0.3%
29.9 3
 
0.3%
21 3
 
0.3%
Other values (484) 565
48.5%
(Missing) 558
47.9%
ValueCountFrequency (%)
-1 1
0.1%
0.2 1
0.1%
0.28 1
0.1%
0.3 1
0.1%
0.34 1
0.1%
0.37 1
0.1%
0.38 1
0.1%
0.39 1
0.1%
0.4 1
0.1%
0.414 1
0.1%
ValueCountFrequency (%)
85.54022989 1
 
0.1%
82 1
 
0.1%
72 1
 
0.1%
63 4
0.3%
60 1
 
0.1%
58.15 2
0.2%
58 1
 
0.1%
58 1
 
0.1%
56.79 1
 
0.1%
56 1
 
0.1%

NH3-N loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct631
Distinct (%)89.5%
Missing460
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean12.774377
Minimum0.02
Maximum160.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:51.781285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.18295868
Q12
median11.126062
Q319.6
95-th percentile32.66
Maximum160.6
Range160.58
Interquartile range (IQR)17.6

Descriptive statistics

Standard deviation13.134421
Coefficient of variation (CV)1.0281849
Kurtosis25.587039
Mean12.774377
Median Absolute Deviation (MAD)8.873938
Skewness3.2135879
Sum9005.9358
Variance172.51302
MonotonicityNot monotonic
2024-04-23T14:03:51.852546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.9 5
 
0.4%
0.11 4
 
0.3%
0.98493 3
 
0.3%
12.7 3
 
0.3%
21.16 3
 
0.3%
38.7 3
 
0.3%
22 3
 
0.3%
21.2 3
 
0.3%
9.6 3
 
0.3%
0.25 3
 
0.3%
Other values (621) 672
57.7%
(Missing) 460
39.5%
ValueCountFrequency (%)
0.02 1
 
0.1%
0.0354 1
 
0.1%
0.04 1
 
0.1%
0.0413 1
 
0.1%
0.06 1
 
0.1%
0.1 1
 
0.1%
0.11 4
0.3%
0.1137694225 1
 
0.1%
0.1159278 1
 
0.1%
0.12155 1
 
0.1%
ValueCountFrequency (%)
160.6 1
0.1%
84.51497682 1
0.1%
75.5 1
0.1%
74.71149316 1
0.1%
74.5876815 1
0.1%
72.7944032 1
0.1%
65 1
0.1%
52.92898646 1
0.1%
50.9 1
0.1%
48.9 1
0.1%

N2O-N loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct398
Distinct (%)74.5%
Missing631
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean1.6451723
Minimum-0.5
Maximum19
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.3%
Memory size9.2 KiB
2024-04-23T14:03:51.915602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.022015
Q10.22859375
median0.715
Q31.976
95-th percentile7.263606
Maximum19
Range19.5
Interquartile range (IQR)1.7474063

Descriptive statistics

Standard deviation2.4258038
Coefficient of variation (CV)1.4744983
Kurtosis8.7544843
Mean1.6451723
Median Absolute Deviation (MAD)0.575
Skewness2.6552344
Sum878.52199
Variance5.884524
MonotonicityNot monotonic
2024-04-23T14:03:51.978136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 10
 
0.9%
0.6 8
 
0.7%
2 8
 
0.7%
0.8 6
 
0.5%
1 6
 
0.5%
1.5 6
 
0.5%
1.4 5
 
0.4%
1.2 5
 
0.4%
0.4 5
 
0.4%
0.19 4
 
0.3%
Other values (388) 471
40.4%
(Missing) 631
54.2%
ValueCountFrequency (%)
-0.5 3
0.3%
0.0006 1
 
0.1%
0.00068 1
 
0.1%
0.000756 1
 
0.1%
0.00189 1
 
0.1%
0.00234 1
 
0.1%
0.0025 1
 
0.1%
0.003 1
 
0.1%
0.004 1
 
0.1%
0.006 1
 
0.1%
ValueCountFrequency (%)
19 1
0.1%
13.05 1
0.1%
12.65063291 1
0.1%
12 1
0.1%
11.22 1
0.1%
11 1
0.1%
10.1722 1
0.1%
9.9 1
0.1%
9.3 1
0.1%
9.103448276 1
0.1%

TC loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct264
Distinct (%)71.4%
Missing795
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean40.571602
Minimum5.1
Maximum92.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:52.056743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile10.3135
Q125.475
median43.785
Q352.8975
95-th percentile65.69
Maximum92.58
Range87.48
Interquartile range (IQR)27.4225

Descriptive statistics

Standard deviation17.615356
Coefficient of variation (CV)0.43417945
Kurtosis-0.46904485
Mean40.571602
Median Absolute Deviation (MAD)11.73
Skewness-0.11069083
Sum15011.493
Variance310.30077
MonotonicityNot monotonic
2024-04-23T14:03:52.119697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.7%
44 5
 
0.4%
51 5
 
0.4%
37.8 4
 
0.3%
40 4
 
0.3%
54 4
 
0.3%
32 4
 
0.3%
53 4
 
0.3%
59.23 4
 
0.3%
43 4
 
0.3%
Other values (254) 324
27.8%
(Missing) 795
68.2%
ValueCountFrequency (%)
5.1 1
0.1%
5.54 1
0.1%
7 1
0.1%
7.04 1
0.1%
7.56 1
0.1%
7.81 1
0.1%
7.9 1
0.1%
8.1 1
0.1%
8.29 1
0.1%
8.3 1
0.1%
ValueCountFrequency (%)
92.58 1
0.1%
88.83 1
0.1%
88.75 1
0.1%
85.83 1
0.1%
80 1
0.1%
79.33 1
0.1%
75.43 1
0.1%
75 1
0.1%
69.1 1
0.1%
68.8 1
0.1%

CH4-C loss (%)
Real number (ℝ)

Distinct277
Distinct (%)69.9%
Missing769
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean1.0944841
Minimum0.001166
Maximum35.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-23T14:03:52.193929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001166
5-th percentile0.0295
Q10.147525
median0.34
Q30.77375
95-th percentile4.5436182
Maximum35.87
Range35.868834
Interquartile range (IQR)0.626225

Descriptive statistics

Standard deviation2.8612458
Coefficient of variation (CV)2.6142415
Kurtosis77.507749
Mean1.0944841
Median Absolute Deviation (MAD)0.24
Skewness7.7826309
Sum433.41571
Variance8.1867274
MonotonicityNot monotonic
2024-04-23T14:03:52.265384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 8
 
0.7%
0.05 7
 
0.6%
0.4 6
 
0.5%
0.12 6
 
0.5%
0.8 6
 
0.5%
0.1 6
 
0.5%
0.13 5
 
0.4%
0.3 5
 
0.4%
0.7 5
 
0.4%
0.27 4
 
0.3%
Other values (267) 338
29.0%
(Missing) 769
66.0%
ValueCountFrequency (%)
0.001166 1
 
0.1%
0.0012 1
 
0.1%
0.001337 1
 
0.1%
0.0018 1
 
0.1%
0.002 1
 
0.1%
0.002336 1
 
0.1%
0.003016 1
 
0.1%
0.008 1
 
0.1%
0.0098 1
 
0.1%
0.01 3
0.3%
ValueCountFrequency (%)
35.87 1
0.1%
25.2 1
0.1%
24.04 1
0.1%
10 1
0.1%
8.409 1
0.1%
7.5 2
0.2%
7.21 1
0.1%
7.2 2
0.2%
6.4 2
0.2%
6.134185304 1
0.1%

CO2-C loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct176
Distinct (%)92.1%
Missing974
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean17.233242
Minimum-0.8
Maximum84.00333
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.3%
Memory size9.2 KiB
2024-04-23T14:03:52.336868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile0.549
Q13.3473599
median12.135153
Q327.095
95-th percentile47.06
Maximum84.00333
Range84.80333
Interquartile range (IQR)23.74764

Descriptive statistics

Standard deviation17.14616
Coefficient of variation (CV)0.99494686
Kurtosis1.8962755
Mean17.233242
Median Absolute Deviation (MAD)9.4671527
Skewness1.3869988
Sum3291.5492
Variance293.99079
MonotonicityNot monotonic
2024-04-23T14:03:52.394165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 2
 
0.2%
32.1 2
 
0.2%
33.3 2
 
0.2%
42.9 2
 
0.2%
45.6 2
 
0.2%
23.9 2
 
0.2%
44.8 2
 
0.2%
42.4 2
 
0.2%
37.2 2
 
0.2%
-0.16 2
 
0.2%
Other values (166) 171
 
14.7%
(Missing) 974
83.6%
ValueCountFrequency (%)
-0.8 1
0.1%
-0.16 2
0.2%
0.1 2
0.2%
0.233 1
0.1%
0.235 1
0.1%
0.293 1
0.1%
0.4537795 1
0.1%
0.47 1
0.1%
0.628 1
0.1%
0.6851181 1
0.1%
ValueCountFrequency (%)
84.00333 1
0.1%
79.56667 1
0.1%
74.43333 1
0.1%
69.3 1
0.1%
60.2 1
0.1%
59 1
0.1%
57.97 1
0.1%
56.949 1
0.1%
52.98333 1
0.1%
48.02 1
0.1%

Interactions

2024-04-23T14:03:49.287143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:41.735487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.363917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.065172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.817919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.532867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.317960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.994691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.608618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.286390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.031155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.641334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.333775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:41.802269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.426794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.179130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.877516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.571983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.364830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.041798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.655490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.333447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.078026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.687584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.380645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:41.849142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.489288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.248786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.924146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:45.427714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.088668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.718371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.380698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:48.734339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.427900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:41.896398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.545200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.309216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.002646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.707119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.482771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.135922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.780361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.443578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.183331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:49.474769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:41.958894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.608087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.371780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.071949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.772309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.545651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.199417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:47.501450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:42.021773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.670585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.450285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.141575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.836972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.608921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.245706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.892115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.553325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.287216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.908177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.568892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.068643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.733462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.497658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.202253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.899346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.671419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.302218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.954994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.609684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.333966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.955050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.616140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.115898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.788390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-23T14:03:44.245602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.955017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.718671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.349131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.002250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.656556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.396461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.017925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.663012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.170363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.845675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.622955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.316711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.017904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.782354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.400044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.073060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.811270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.459340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.064835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.718808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.230842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.908549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.669823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.376952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.080396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.845280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.458919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.120397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.873767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.506590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.127675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.750440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.280371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.955421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.728863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.412238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.137565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.892152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.506171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.182890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.921026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.553461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.184143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.811251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:42.326659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.018302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:43.785345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:44.474113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.200439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:45.939444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:46.560887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.230142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:47.983900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:48.600560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-23T14:03:49.231400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-04-23T14:03:52.457164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Application Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)material_0material_1Excipients_1Additive Species
Application Rate (%)1.0000.113-0.036-0.349-0.2490.121-0.0060.0260.2420.147-0.1450.3230.2160.2840.5570.230
initial moisture content(%)0.1131.0000.1230.1510.072-0.2040.3020.049-0.0120.3240.097-0.3260.1610.1990.2770.287
initial pH-0.0360.1231.000-0.0910.0370.024-0.052-0.001-0.1230.0130.0030.0090.2080.1050.3040.141
initial TN(%)-0.3490.151-0.0911.0000.278-0.6690.251-0.061-0.2430.001-0.042-0.1720.1220.1560.2340.084
initial TC(%)-0.2490.0720.0370.2781.0000.187-0.258-0.290-0.217-0.180-0.007-0.5310.1100.1080.2470.091
initial CN(%)0.121-0.2040.024-0.6690.1871.000-0.2310.1320.285-0.0750.068-0.1310.1720.2010.4050.238
TN loss (%)-0.0060.302-0.0520.251-0.258-0.2311.0000.6720.4520.493-0.0560.4040.1340.1720.3840.138
NH3-N loss (%)0.0260.049-0.001-0.061-0.2900.1320.6721.0000.3960.2870.0630.4140.0000.1130.3850.141
N2O-N loss (%)0.242-0.012-0.123-0.243-0.2170.2850.4520.3961.0000.5120.3140.3150.1240.0910.2640.158
TC loss (%)0.1470.3240.0130.001-0.180-0.0750.4930.2870.5121.0000.2210.6350.1510.2240.4940.300
CH4-C loss (%)-0.1450.0970.003-0.042-0.0070.068-0.0560.0630.3140.2211.0000.1140.0000.0000.0640.340
CO2-C loss (%)0.323-0.3260.009-0.172-0.531-0.1310.4040.4140.3150.6350.1141.0000.2650.3510.3710.276
material_00.2160.1610.2080.1220.1100.1720.1340.0000.1240.1510.0000.2651.0000.9950.7280.111
material_10.2840.1990.1050.1560.1080.2010.1720.1130.0910.2240.0000.3510.9951.0000.7330.141
Excipients_10.5570.2770.3040.2340.2470.4050.3840.3850.2640.4940.0640.3710.7280.7331.0000.393
Additive Species0.2300.2870.1410.0840.0910.2380.1380.1410.1580.3000.3400.2760.1110.1410.3931.000

Missing values

2024-04-23T14:03:49.889369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-23T14:03:49.999157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-23T14:03:50.140537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

material_0material_1Excipients_1Additive SpeciesApplication Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)
0ManureSwine manureCorn stalkNaNNaN65.07.606642.66039.9015.035.028.00NaN50.0NaNNaN
1ManureSwine manureCorn stalkChemical0.40965.07.410842.66039.9015.012.09.00NaN53.0NaNNaN
2ManureSwine manureCorn stalkChemical0.49765.06.615382.66039.9015.05.04.00NaN55.0NaNNaN
3ManureSwine manureCorn stalkChemical0.45465.06.407342.66039.9015.01.00.50NaN43.0NaNNaN
4ManureCow manureSawdustNaNNaNNaN7.520001.31047.1636.016.07.26NaNNaNNaNNaN
5ManureCow manureSawdustPhysical17.000NaN7.580001.140NaNNaN6.13.59NaNNaNNaNNaN
6Sewage sludgeNaNSawdustNaNNaNNaN7.700002.850NaNNaN35.115.53NaNNaNNaNNaN
7Sewage sludgeNaNSawdustPhysical17.000NaN7.400001.85035.1519.011.413.53NaNNaNNaNNaN
8ManureCow manureSawdustNaNNaN61.77.870000.98450.6051.5-1.06.79NaNNaNNaNNaN
9ManureCow manureSawdustPhysical6.00061.47.940001.12049.9044.64.52.95NaNNaNNaNNaN
material_0material_1Excipients_1Additive SpeciesApplication Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)
1155NaNNaNNaNNaNNaN76.28NaNNaN78.270000NaNNaN0.1500.328NaN0.8003.754
1156NaNNaNNaNNaNNaN82.52NaN3.2000086.88000027.150000NaN0.4120.223NaN0.3601.312
1157NaNNaNNaNNaNNaN77.67NaN2.92000123.79000042.393836NaN0.1280.432NaN0.2707.299
1158NaNNaNNaNNaNNaN72.04NaN2.60000178.23000022.351349NaN0.2230.168NaN0.1808.177
1159NaNNaNNaNNaNNaN45.327.352.84514174.91000012.013049NaNNaN0.140NaN0.4102.818
1160NaNNaNNaNNaNNaN57.908.1410.2700056.40765131.372931NaNNaN0.380NaN0.25011.935
1161NaNNaNNaNNaNNaN62.908.202.8451456.40765125.006729NaN0.740NaNNaNNaN0.100
1162NaNNaNNaNNaNNaN62.908.202.8451456.40765125.006729NaN0.351NaNNaNNaN0.100
1163NaNNaNNaNNaNNaN59.669.00NaNNaNNaNNaNNaN0.168NaN0.7850.235
1164NaNNaNNaNNaNNaN76.008.203.8900069.21000017.791774NaN1.5001.100NaN2.9405.635

Duplicate rows

Most frequently occurring

material_0material_1Excipients_1Additive SpeciesApplication Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)# duplicates
36ManureSwine manureNaNChemicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3
0Food wasteNaNNaNChemicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
1ManureCow manurePine SawdustPhysical23.073.95107.600.6832.3347.528.2718.6685086.56482253.04NaNNaN2
2ManureCow manurePine SawdustNaNNaN75.57467.700.8540.4147.544.1521.7647417.11157061.65NaNNaN2
3ManureCow manureRice strawPhysical23.055.09168.000.8029.7337.146.3126.7222376.73817755.46NaNNaN2
4ManureCow manureRice strawNaNNaN62.35088.201.0037.1637.258.1522.3646254.24658664.61NaNNaN2
5ManurePoultry manurePlant strawPhysical5.060.00007.131.7051.0030.08.15NaNNaN37.80NaNNaN2
6ManurePoultry manurePlant strawPhysical5.060.00007.161.8245.5025.016.31NaNNaN39.60NaNNaN2
7ManurePoultry manurePlant strawPhysical5.060.00007.481.6348.9030.016.99NaNNaN40.50NaNNaN2
8ManurePoultry manurePlant strawPhysical5.060.00007.691.7844.5025.018.35NaNNaN30.60NaNNaN2